Abstract
It has long been appreciated that the statistical properties of natural stimuli shape neural processing mechanisms in the sensory periphery, but the extent to which such a principle can be formulated for and applied to central processing is unclear. The periphery faces a transmission bottleneck, so efficiency implies compression of signal components with a predictably wider range. Cortex faces a different challenge – it must use limited samples to make inferences to guide decisions. In this regime, efficient coding predicts the opposite from the periphery: that greater resources are allocated to the signal components with a wider range. To test this hypothesis, we carry out two parallel studies. In one, we measure the joint distribution of local two-, three-, and four-point spatial correlations in an ensemble of natural images. In the other, we measure human perceptual sensitivity to these correlations and their combinations via psychophysical experiments that use synthetic visual textures. We show that psychophysical performance, described by dozens of independent parameters, can be predicted with surprising accuracy from the distribution of spatial correlations found in the natural images. Thus, the efficient coding principle extends beyond the sensory periphery to the central nervous system, where it applies in a very different guise and accounts for the sensitivity to higher-order elements of visual form.